SWOT: Through a GIS Lens

The  Surface Water and Ocean Topography (SWOT)  mission is looking to measure ocean features at unprecedented scales and surface water over land (i.e. extents and elevations of rivers, lakes, and reservoirs). It is a joint venture by both NASA and the French space agency  Centre National d'Études Spatiales  (CNES) with contributions from the  Canadian Space Agency  (CSA) and  United Kingdom Space Agency  focused on better understanding the world's oceans and its terrestrial surface waters. ¹  The SWOT mission was successfully launched on December 16th, 2022.

The NASA  Physical Oceanography Distributed Active Archive Center  (PO.DAAC) is housing all of the  SWOT mission datasets . SWOT introduced PO.DAAC's first geospatial vector data products hosted in shapefile format, specifically the terrestrial hydrology surface water products. Geographic Information System (GIS) helps analyze and display such geospatial datasets.

There are various routes to take when looking to visualize geospatial data. Well known GIS desktop software includes  ESRI's  ArcMap and ArcGIS Pro.  ESRI’s ArcGIS Pro  offers the capability of visualizing geospatial data, but there can be a cost associated with the tools. For an open-source alternative to help visualize geospatial data,  QGIS  is a leading open-source desktop GIS software. There is also the programming route of using the open-source scripting language Python. Python offers geospatial libraries that utilize similar analysis and geoprocessing tools as those found in GIS desktop softwares.  Jupyter Notebook  is an open-source platform that allows you to run and visualize geospatial data through the use of Python.

The SWOT mission has multiple products available for terrestrial hydrology. This includes  terrestrial water bodies  (lakes, reservoirs, wetlands) whose surface area exceeds 250 m by 250 m (~820 ft by ~820 ft).  River reaches  that are wider than 100 m (328 ft). These products are available at PODAAC to users as a  pixel cloud ,  raster , and vector product in both shapefile and NetCDF format.

Measuring the global storage change in freshwater bodies at sub-monthly, seasonal, and annual time scales. Estimating the global change in river discharge at sub-monthly, seasonal, and annual time scales.

In addition, the SWOT River Database ( SWORD ) developed by the SWOT Science Team serves as the foundation for SWOT river data products. It is based on a variety of datasets, including the Global River Widths from Landsat (GRWL) database, MERIT-Hydro, and the Global River Obstruction Database (GROD). It consists of a series of river nodes (~200 m spacing) and reaches (~10 km long) for which SWOT data will be provided, and is available as shapefile, geopackage and NetCDF which can be accessed  here . If you would like more information about SWORD, please see the  Altenau et al, 2021  paper published in Water Resources Research.

This StoryMap provides examples for exploring SWOT hydrology datasets, of river reach products that are available from NASA PO.DAAC, respectively, using ArcGIS Pro, QGIS, and Jupyter notebooks in Python.

One option is to download the SWOT data from  Earthdata Search . Once you have the data locally, you have the option to visualize based on your software preference of either ArcGIS Pro or QGIS. Both of these softwares can easily add and visualize shapefile data. The SWOT Raster product which is in NetCDF file formats can be visualized with ease. ArcGIS Pro offers the 'Add Multidimensional Raster Layer' tool which gives you the option to pick which variable to visualize or combine multiple variables to a single layer. QGIS offers a simpler breakdown of the variables within its directory browser allowing you to choose which variable to visualize. Below are image examples of visualizing SWOT data in ArcGIS Pro and QGIS.

The application below shows SWOT river reach data of North America from February 2024 that has been rendered within ArcGIS Experience Builder.

This exploratory tool gives users the option to draw a bounding box of a desired area with the selection tool. The selected area will be highlighted on the map but also shown in the attribute table. With this selection, it gives you the option to export the selected dataset as a JSON, CSV, or GeoJSON. You also have the option to visualize the river reaches with various available basemaps.

Jupyter Notebook along with Python offers the versatility of a GIS desktop software without having to have the application on your computer, with many available open-source libraries that let you visualize and analyze datasets. Some examples include  GeoPandas  to read geospatial data,  Matplotlib  for visualization, or  Contextily  which provides basemaps to your map.

The notebook below offers users the ability to read, visualize and further analyze SWOT river reach shapefile datasets locally. You can also access (and clone) the notebook from the PO.DAAC GitHub,  https://github.com/podaac/tutorials/blob/master/notebooks/GIS/SWOT_GISshapefiles.ipynb .

The 2nd notebook below offers users the ability to read and create a time dimension with multiple SWOT Raster image data products. You can also access (and clone) the notebook from the PO.DAAC GitHub,  https://github.com/podaac/tutorials/blob/master/notebooks/datasets/SWOT_Raster_Notebook_local.ipynb .

The following notebook showcases how to transform the datetime field for use in GIS softwares. You can also access (and clone) the notebook from the PO.DAAC GitHub,  https://github.com/podaac/tutorials/blob/master/notebooks/GIS/SWOT_datetime_GIS.ipynb .

NASA is planning to offer GIS-ready datasets through the development of Earthdata GIS (EGIS). At PO.DAAC the SWOT river reach product is currently being developed to be offered as a web feature service (WFS) that can be plugged into your favorite GIS software.

On March 6th, 2024, the  SWOT Ka-band Radar Interferometer (KaRIn) Science Data Products  was released to the public and is made available at PO.DAAC.

For more information please visit  PO.DAAC  or the  SWOT  homepage.

Also, visit the  PO.DAAC StoryMap Collection  page to learn about other datasets.